{"id":"W4402533451","doi":"10.1093/jas/skae234.283","title":"377 Developing visible near-infrared spectroscopy calibration equations to predict the chemical composition of feces and nutrient digestibility based on pig fecal spectra","year":2024,"lang":"en","type":"article","venue":"Journal of Animal Science","topic":"Animal Nutrition and Health","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Feces; Composition (language); Nutrient; Chemistry; Calibration; Infrared spectroscopy; Analytical Chemistry (journal); Environmental chemistry; Biology; Mathematics; Ecology; Statistics; Organic chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008480373,0.00007768705,0.0001299158,0.00003556419,0.0003272575,0.0002329541,0.0001991259,0.00003360204,0.00004260565],"category_scores_gemma":[0.00013046,0.00002923518,0.0000523106,0.0007838908,0.0002625444,0.0002928008,0.00003439079,0.0001576798,0.000002141626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001140435,"about_ca_system_score_gemma":0.0001588798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000229823,"about_ca_topic_score_gemma":0.000009630695,"domain_scores_codex":[0.9987754,0.00005113552,0.0003145699,0.0001864549,0.0004944144,0.0001780268],"domain_scores_gemma":[0.9992636,0.000311055,0.0001084963,0.000033386,0.0001329354,0.0001505508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002388824,0.0000821312,0.001464172,0.00001667915,0.000001851971,0.000002371318,0.0001057949,0.00001916543,0.990811,0.006553255,0.0001845881,0.0005201388],"study_design_scores_gemma":[0.0002928771,0.006115994,0.5046498,0.0005840528,0.00002341857,0.00004153548,0.0003152571,0.05198251,0.4310385,0.00380117,0.0009443812,0.0002104841],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9834196,0.00007000446,0.00215453,0.01391874,0.00007439077,0.0001635172,0.00001516691,0.00001578464,0.0001682784],"genre_scores_gemma":[0.9967496,0.00002119948,0.002676299,0.0003880055,0.0001582171,0.000001572108,0.000002306116,6.303451e-7,0.000002208643],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5597725,"threshold_uncertainty_score":0.2517033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02966138298395583,"score_gpt":0.2951952447271006,"score_spread":0.2655338617431448,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}